On the scattering length of the K^- d system

Multiple-scattering approximations to Faddeev calculations of the K^- d scattering length are reviewed and compared with published Kbar-N-N pi-Y-N fully reactive Faddeev calculations. A new multiple-scattering approximation which goes beyond the `fixed-center' assumption for the nucleons is proposed, aiming at accuracies of 5-10%. A precise value of the K^- d scattering length from the measurement of the K^- d 1s atomic level shift and width, planned by the DEAR/SIDDHARTA collaboration, plus a precise value for the K^- p scattering length by improving the K^- p atom measurements, are essential for extracting the K^- n scattering length, for resolving persistent puzzles in low-energy Kbar-N phenomenology and for extrapolating into Kbar-nuclear systems.Comment: Invited talk at MESON 2006, Krakow, June 2006. To be published in International Journal of Modern Physics A. Requires use of ws-ijmpa.cl

Charge domain filter operating up to 20 MHz clock frequency

An analog sampled data low pass third order Butterworth filter has been realised in a buried channel CCD technology. This Charge Domain Filter, composed of transversal and recursive CCD filter sections, has been tested at clock frequencies up to 20 MHz

On Some Geometric Properties of Slice Regular Functions of a Quaternion Variable

The goal of this paper is to introduce and study some geometric properties of slice regular functions of quaternion variable like univalence, subordination, starlikeness, convexity and spirallikeness in the unit ball. We prove a number of results, among which an Area-type Theorem, Rogosinski inequality, and a Bieberbach-de Branges Theorem for a subclass of slice regular functions. We also discuss some geometric and algebraic interpretations of our results in terms of maps from $\mathbb R^4$ to itself. As a tool for subordination we define a suitable notion of composition of slice regular functions which is of independent interest

Longtime behavior of nonlocal Cahn-Hilliard equations

Here we consider the nonlocal Cahn-Hilliard equation with constant mobility in a bounded domain. We prove that the associated dynamical system has an exponential attractor, provided that the potential is regular. In order to do that a crucial step is showing the eventual boundedness of the order parameter uniformly with respect to the initial datum. This is obtained through an Alikakos-Moser type argument. We establish a similar result for the viscous nonlocal Cahn-Hilliard equation with singular (e.g., logarithmic) potential. In this case the validity of the so-called separation property is crucial. We also discuss the convergence of a solution to a single stationary state. The separation property in the nonviscous case is known to hold when the mobility degenerates at the pure phases in a proper way and the potential is of logarithmic type. Thus, the existence of an exponential attractor can be proven in this case as well

Revealing hidden scenes by photon-efficient occlusion-based opportunistic active imaging

The ability to see around corners, i.e., recover details of a hidden scene from its reflections in the surrounding environment, is of considerable interest in a wide range of applications. However, the diffuse nature of light reflected from typical surfaces leads to mixing of spatial information in the collected light, precluding useful scene reconstruction. Here, we employ a computational imaging technique that opportunistically exploits the presence of occluding objects, which obstruct probe-light propagation in the hidden scene, to undo the mixing and greatly improve scene recovery. Importantly, our technique obviates the need for the ultrafast time-of-flight measurements employed by most previous approaches to hidden-scene imaging. Moreover, it does so in a photon-efficient manner based on an accurate forward model and a computational algorithm that, together, respect the physics of three-bounce light propagation and single-photon detection. Using our methodology, we demonstrate reconstruction of hidden-surface reflectivity patterns in a meter-scale environment from non-time-resolved measurements. Ultimately, our technique represents an instance of a rich and promising new imaging modality with important potential implications for imaging science.Comment: Related theory in arXiv:1711.0629

An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion

Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific unique concepts, modify their appearance, or compose them in new roles and novel scenes. In other words, we ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom. Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new "words" in the embedding space of a frozen text-to-image model. These "words" can be composed into natural language sentences, guiding personalized creation in an intuitive way. Notably, we find evidence that a single word embedding is sufficient for capturing unique and varied concepts. We compare our approach to a wide range of baselines, and demonstrate that it can more faithfully portray the concepts across a range of applications and tasks. Our code, data and new words will be available at: https://textual-inversion.github.ioComment: Project page: https://textual-inversion.github.i

Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models

Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encoder-based techniques have emerged as a new effective approach for T2I personalization, reducing the need for multiple images and long training times. However, most existing encoders are limited to a single-class domain, which hinders their ability to handle diverse concepts. In this work, we propose a domain-agnostic method that does not require any specialized dataset or prior information about the personalized concepts. We introduce a novel contrastive-based regularization technique to maintain high fidelity to the target concept characteristics while keeping the predicted embeddings close to editable regions of the latent space, by pushing the predicted tokens toward their nearest existing CLIP tokens. Our experimental results demonstrate the effectiveness of our approach and show how the learned tokens are more semantic than tokens predicted by unregularized models. This leads to a better representation that achieves state-of-the-art performance while being more flexible than previous methods.Comment: Project page at https://datencoder.github.i

Ultrafast trapping times in ion implanted InP

As⁺ and P⁺implantation was performed on semi-insulating (SI) and p-type InP samples for the purpose of creating a material suitable for ultrafast optoelectronic applications. SI InP samples were implanted with a dose of 1×10¹⁶ cm⁻² and p-type InP was implanted with doses between 1×10¹² and 1×10¹⁶ cm⁻². Subsequently, rapid thermal annealing at temperatures between 400 and 700 °C was performed for 30 sec. Hall-effect measurements, double-crystal x-ray diffraction, and time-resolved femtosecond differential reflectivity showed that, for the highest-annealing temperatures, the implanted SI InP samples exhibited high mobility, low resistivity, short response times, and minimal structural damage. Similar measurements on implantedp-type InP showed that the fast response time, high mobility, and good structural recovery could be retained while increasing the resistivity